Optimizing selected variables of an optical metrology model

ABSTRACT

An optical metrology model is created for a patterned structure formed on a semiconductor wafer. The optical metrology model has profile parameters, material refraction parameters, and metrology device parameters. Ranges of values for the parameters are defined. One or more measured diffraction signals of the patterned structure are obtained. The optical metrology model is optimized to obtain an optimized optical metrology model using the defined ranges of values defined and the one or more obtained measured diffraction signals of the patterned structure. For at least one parameter from amongst the material refraction parameters and the metrology device parameters, the at least one parameter is set to a fixed value within the range of values for the at least one parameter. At least one profile parameter of the patterned structure is determined using the optimized optical metrology model and the fixed value for the at least one parameter.

BACKGROUND

1. Field

The present application generally relates to optical metrology of astructure formed on a semiconductor wafer, and, more particularly, tooptical metrology of patterned structures.

2. Related Art

In semiconductor manufacturing, periodic gratings are typically used forquality assurance. For example, one typical use of periodic gratingsincludes fabricating a periodic grating in proximity to the operatingstructure of a semiconductor chip. The periodic grating is thenilluminated with an electromagnetic radiation. The electromagneticradiation that deflects off of the periodic grating are collected as adiffraction signal. The diffraction signal is then analyzed to determinewhether the periodic grating, and by extension whether the operatingstructure of the semiconductor chip, has been fabricated according tospecifications.

In one conventional system, the diffraction signal collected fromilluminating the periodic grating (the measured-diffraction signal) iscompared to a library of simulated-diffraction signals. Eachsimulated-diffraction signal in the library is associated with ahypothetical profile. When a match is made between themeasured-diffraction signal and one of the simulated-diffraction signalsin the library, the hypothetical profile associated with thesimulated-diffraction signal is presumed to represent the actual profileof the periodic grating.

The library of simulated-diffraction signals can be generated using arigorous method, such as rigorous coupled wave analysis (RCWA). Moreparticularly, in the diffraction modeling technique, asimulated-diffraction signal is calculated based, in part, on solvingMaxwell's equations. Calculating the simulated diffraction signalinvolves performing a large number of complex calculations, which can betime consuming and costly.

SUMMARY

In one exemplary embodiment, to examine a patterned structure formed ona semiconductor wafer using an optical metrology model, an opticalmetrology model is created for the patterned structure. The opticalmetrology model has profile parameters, material refraction parameters,and metrology device parameters. Ranges of values for the profileparameters, material refraction parameters, and metrology deviceparameters are defined. One or more measured diffraction signals of thepatterned structure are obtained. The optical metrology model isoptimized to obtain an optimized optical metrology model using thedefined ranges of values defined and the one or more obtained measureddiffraction signals of the patterned structure. For at least oneparameter from amongst the material refraction parameters and themetrology device parameters, the at least one parameter is set to afixed value within the range of values for the at least one parameter.At least one profile parameter of the patterned structure is determinedusing the optimized optical metrology model and the fixed value for theat least one parameter.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A is an architectural diagram illustrating an exemplary embodimentwhere optical metrology can be utilized to determine the profiles ofstructures on a semiconductor wafer.

FIG. 1B depicts an exemplary one-dimension repeating structure.

FIG. 1C depicts an exemplary two-dimension repeating structure

FIG. 2A depicts exemplary orthogonal grid of unit cells of atwo-dimension repeating structure.

FIG. 2B depicts a top-view of a two-dimension repeating structure.

FIG. 2C is an exemplary technique for characterizing the top-view of atwo-dimension repeating structure.

FIG. 3 is an exemplary flowchart for determining profile parameters ofwafer structures using obtained values of optical metrology variables.

FIG. 4A is an exemplary flowchart of techniques to obtain refractionindices for wafer structures.

FIG. 4B is an exemplary flowchart for obtaining values for metrologydevice variables.

FIG. 5 is an exemplary architectural diagram of an embodiment for a realtime profile estimator.

FIG. 6 is an exemplary architectural diagram of an embodiment forcreating and using a profile server data store.

FIG. 7 is an exemplary architectural diagram for linking two or morefabrication systems with a metrology processor and a metrology datastore to determine profile parameters of patterned structures.

FIG. 8 is an exemplary flowchart for managing and utilizing metrologydata for automated process and equipment control.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENT(S)

In order to facilitate the description of the present invention, asemiconductor wafer may be utilized to illustrate an application of theconcept. The methods and processes equally apply to other work piecesthat have repeating structures. Furthermore, in this application, theterm structure when it is not qualified refers to a patterned structure.

FIG. 1A is an architectural diagram illustrating an exemplary embodimentwhere optical metrology can be utilized to determine the profiles ofstructures on a semiconductor wafer. The optical metrology system 40includes a metrology beam source 41 projecting a beam 43 at the targetstructure 59 of a wafer 47. The metrology beam 43 is projected at anincidence angle θ_(i) towards the target structure 59, and diffracts ata diffraction angle θ_(d). The diffraction beam 49 is measured by ametrology beam receiver 51. The diffraction beam data 57 is transmittedto a profile application server 53. The profile application server 53compares the measured diffraction beam data 57 against a library 60 ofsimulated diffraction beam data representing varying combinations ofcritical dimensions of the target structure and resolution. In oneexemplary embodiment, the library 60 instance best matching the measureddiffraction beam data 57 is selected. It is understood that although alibrary of diffraction spectra or signals and associated hypotheticalprofiles is frequently used to illustrate concepts and principles, thepresent invention equally applies to a data space comprising simulateddiffraction signals and associated set of profile parameters, such as inregression, neural net, and similar methods used for profile extraction.The hypothetical profile and associated critical dimensions of theselected library 60 instance is assumed to correspond to the actualcross-sectional profile and critical dimensions of the features of thetarget structure 59. The optical metrology system 40 may utilize areflectometer, an ellipsometer, or other optical metrology device tomeasure the diffraction beam or signal. An optical metrology system isdescribed in U.S. Pat. No. 6,913,900, entitled GENERATION OF A LIBRARYOF PERIODIC GRATING DIFFRACTION SIGNAL, by Niu, et al., issued on Sep.13, 2005, and is incorporated in its entirety herein by reference. Otherexemplary embodiments of the present invention in optical metrology notrequiring the use of libraries are discussed below.

An alternative is to generate the library of simulated-diffractionsignals using a machine learning system (MLS). Prior to generating thelibrary of simulated-diffraction signals, the MLS is trained using knowninput and output data. In one exemplary embodiment, simulateddiffraction signals can be generated using a machine learning system(MLS) employing a machine learning algorithm, such as back-propagation,radial basis function, support vector, kernel regression, and the like.For a more detailed description of machine learning systems andalgorithms, see “Neural Networks” by Simon Haykin, Prentice Hall, 1999,which is incorporated herein by reference in its entirety. See also U.S.patent application Ser. No. 10/608,300, titled OPTICAL METROLOGY OFSTRUCTURES FORMED ON SEMICONDUCTOR WAFERS USING MACHINE LEARNINGSYSTEMS, filed on Jun. 27, 2003, which is incorporated herein byreference in its entirety.

The term “one-dimension structure” is used herein to refer to astructure having a profile that varies only in one dimension. Forexample, FIG. 1B depicts a periodic grating having a profile that variesin one dimension (i.e., the x-direction). The profile of the periodicgrating depicted in FIG. 1B varies in the z-direction as a function ofthe x-direction. However, the profile of the periodic grating depictedin FIG. 1B is assumed to be substantially uniform or continuous in they-direction.

The term “two-dimension structure” is used herein to refer to astructure having a profile that varies in at least two-dimensions. Forexample, FIG. 1C depicts a periodic grating having a profile that variesin two dimensions (i.e., the x-direction and the y-direction). Theprofile of the periodic grating depicted in FIG. 1C varies in they-direction.

Discussion for FIGS. 2A, 2B, and 2C below describe the characterizationof two-dimension repeating structures for optical metrology modeling.FIG. 2A depicts a top-view of exemplary orthogonal grid of unit cells ofa two-dimension repeating structure. A hypothetical grid of lines issuperimposed on the top-view of the repeating structure where the linesof the grid are drawn along the direction of periodicity. Thehypothetical grid of lines forms areas referred to as unit cells. Theunit cells may be arranged in an orthogonal or non-orthogonalconfiguration. Two-dimension repeating structures may comprise featuressuch as repeating posts, contact holes, vias, islands, or combinationsof two or more shapes within a unit cell. Furthermore, the features mayhave a variety of shapes and may be concave or convex features or acombination of concave and convex features. Referring to FIG. 2A, therepeating structure 300 comprises unit cells with holes arranged in anorthogonal manner. Unit cell 302 includes all the features andcomponents inside the unit cell 302, primarily comprising a hole 304substantially in the center of the unit cell 302.

FIG. 2B depicts a top-view of a two-dimension repeating structure. Unitcell 310 includes a concave elliptical hole. FIG. 2B shows a unit cell310 with a feature 320 that comprises an elliptical hole wherein thedimensions become progressively smaller until the bottom of the hole.Profile parameters used to characterize the structure includes theX-pitch 312 and the Y-pitch 314. In addition, the major axis of theellipse 316 that represents the top of the feature 320 and the majoraxis of the ellipse 318 that represents the bottom of the feature 320may be used to characterize the feature 320. Furthermore, anyintermediate major axis between the top and bottom of the feature mayalso be used as well as any minor axis of the top, intermediate, orbottom ellipse, (not shown).

FIG. 2C is an exemplary technique for characterizing the top-view of atwo-dimension repeating structure. A unit cell 330 of a repeatingstructure is a feature 332, an island with a peanut-shape viewed fromthe top. One modeling approach includes approximating the feature 332with a variable number or combinations of ellipses and polygons. Assumefurther that after analyzing the variability of the top-view shape ofthe feature 322, it was determined that two ellipses, Ellipsoid 1 andEllipsoid 2, and two polygons, Polygon 1 and Polygon 2 were found tofully characterize feature 332. In turn, parameters needed tocharacterize the two ellipses and two polygons comprise nine parametersas follows: T1 and T2 for Ellipsoid 1; T3, T4, and θ₁ for Polygon 1; T4.T5, and θ₂ for Polygon 2; T6 and T7 for Ellipsoid 2. Many othercombinations of shapes could be used to characterize the top-view of thefeature 332 in unit cell 330. For a detailed description of modelingtwo-dimension repeating structures, refer to U.S. patent applicationSer. No. 11/061,303, OPTICAL METROLOGY OPTIMIZATION FOR REPETITIVESTRUCTURES, by Vuong, et al., filed on Apr. 27, 2004, and isincorporated in its entirety herein by reference.

FIG. 3 is an exemplary flowchart for examining a patterned structureformed on a semiconductor wafer. Referring to FIG. 3, in step 400, anoptical metrology model of the patterned structure is created. Theoptical metrology model includes parameters characterizing the profileof the patterned structure (i.e., profile parameters), parametersrelated to the material refraction used in the layers of the structure(i.e., material refraction parameters), and parameters related to themetrology device and angular settings of the illumination beam relativeto the repeating structure (i.e., metrology device parameters).

As mentioned above, profile parameters can include height, width,sidewall angle, and characterization of profile features, such astop-rounding, T-topping, footing, and the like. Also mentioned above,profile parameters for repeating structures can include X-pitch andY-pitch of the unit cell, major and minor axes of ellipses anddimensions of polygons used to characterize the top-view shape of holeor island, and the like.

Still referring to FIG. 3, material refraction parameters include therefractive index, N parameter, and the extinction coefficient, Kparameter, as represented in the following equations:

$\begin{matrix}{{N\left( {\lambda,a} \right)} = {a_{1} + \frac{a_{2}}{\lambda^{2}} + \frac{a_{3}}{\lambda^{4}}}} & 1.1 \\{{K\left( {\lambda,b} \right)} = {\frac{b_{1}}{\lambda} + \frac{b_{2}}{\lambda^{3}} + \frac{b_{3}}{\lambda^{5}}}} & 1.2 \\{a = \left\lbrack \begin{matrix}{a_{1},} & {a_{2},} & \left. a_{3} \right\rbrack\end{matrix} \right.} & 1.3 \\{b = \left\lbrack \begin{matrix}{b_{1},} & {b_{2},} & b_{3\rbrack}\end{matrix} \right.} & 1.4\end{matrix}$where λ is the wavelength, a is the refractive index constant for thematerial, and b is extinction coefficient constant for the material.Instead of floating N and K, the constants a and b can be floated in theoptical metrology model.

In step 402, the ranges of profile parameters, material refractionparameters, and metrology device parameters are defined. In one example,ranges of the material refraction parameters (e.g., N and K parameters)and the metrology device parameters (e.g., angle of incidence andazimuth angle of the incident beam relative to the direction ofperiodicity of the repeating structure) are defined. As noted above,constants a and b can be used for the N and K parameters.

In step 404, a measured diffraction signal is obtained, where themeasured diffraction signal was measured off the patterned structureusing an optical metrology device. In one example, a particular opticalmetrology device can be selected and used to obtain the measureddiffraction signal. The optical metrology device may be a reflectometer,ellipsometer, hybrid reflectometer/ellipsometer, and the like.

In step 406, the optical metrology model is optimized using the measureddiffraction signal and ranges of the profile parameters, materialrefraction parameters, and metrology device parameters. For example, aninitial optical metrology model can be defined. One or more simulateddiffraction signals can be generated for the initial optical metrologymodel using values for the profile parameters, material refractionparameters, and metrology device parameters within the ranges defined instep 402. The one or more simulated diffraction signals can be comparedto the measured diffraction signal. The results of this comparison canbe evaluated using one or more termination criteria, such as a costfunction, goodness of fit (GOF), and the like. If the one or moretermination criteria are not met, the initial optical metrology modelcan then be altered to generate a refined optical metrology model. Theprocess of generating one or more diffraction signals and comparing theone or more diffraction signals to the measured diffraction signal canbe repeated. This process of altering the optical metrology model can berepeated until the one or more termination criteria are met to obtain anoptimized metrology model. For detailed description of metrology modeloptimization, refer to U.S. patent application Ser. No. 10/206,491,OPTIMIZED MODEL AND PARAMETER SELECTION FOR OPTICAL METROLOGY, by Vuong,et al., filed Jun. 27, 2002; Ser. No. 10/946,729, OPTICAL METROLOGYMODEL OPTIMIZATION BASED ON GOALS, by Vuong, et al., filed Sep. 21,2004; and U.S. patent application Ser. No. 11/061,303, OPTICAL METROLOGYOPTIMIZATION FOR REPETITIVE STRUCTURES, by Vuong, et al., filed on Apr.27, 2004, all of which are incorporated herein by reference in theirentireties.

In step 408, for at least one parameter from amongst the materialrefraction parameters, and the metrology device parameters, at least oneparameter is set to a fixed value within the range of values for the atleast one parameter. FIGS. 4A and 4B are exemplary flowcharts oftechniques to obtain values of parameters of the optical metrologymodel, which can be used as the fixed values in step 408.

FIG. 4A is an exemplary flowchart of techniques to obtain values of theN and K parameters. In step 500, the N and K parameters, includingconstants a and b, are obtained from empirical data, such as similardata from previous wafer structures using the same materials, historicalvalues of the constants from previous runs of the same recipe and frompublications or handbooks. In step 510, the N and K parameters,including constants a and b, are obtained from measurements using theoptical metrology device integrated with a fabrication equipment, suchas an etch or a track integrated fabrication equipment. In step 520, theN and K parameters, including constants a and b, are obtained usingoffline optical metrology devices.

In one embodiment, the site measured in step 520 is an unpatterned areaadjacent to the patterned structure. In another embodiment, the sitemeasured is not adjacent to the patterned structure and may be in a testarea of the same wafer or in an area of a test wafer. In anotherembodiment, one site is measured per wafer, or per lot and the constantsa and b obtained are used for the same wafer, for the whole lot ofwafers, or for a whole process run. Alternatively, a previouscorrelation of the thickness of the layer and the constants a and b maybe used to obtain the values of the constants a and b once the thicknessof the layer is determined.

Referring to FIG. 4A, in step 540, the material data obtained fromvarious sources and using various techniques are processed for use inthe profile determination of the patterned structure. For example, ifseveral measurements are made to determine the constants a and b, astatistical average may be calculated.

FIG. 4B is a flowchart for obtaining values for metrology deviceparameters. In one embodiment, in step 600, based on a selectedmetrology device, the angle of incidence of the illumination beam isobtained from the vendor specifications or from the setting used for theapplication if the metrology device has a variable angle of incidence.Similarly, in step 610, the azimuth angle may be determined based on theselected optical metrology device and the wafer structure application.In step 640, the process device specifications and settings data foroptical metrology are processed. Given a reflectometer with normalincidence or an ellipsometer with a fixed angle of incidence as theselected metrology device, the normal incidence or the fixed angle isconverted into the format required for the optical metrology model.Similarly, if the azimuth angle of the metrology device is alsoconverted into the format required for the optical metrology model.

Referring to FIG. 3, in step 410, the profile of the patterned structurecan be determined using the optimized optical metrology model and thefixed value in step 408. In particular, at least one profile parameterof the patterned structure is determined using the optimized opticalmetrology model and the fixed value in step 408. The at least oneprofile parameter can be determined using a regression process or alibrary-based process.

As mentioned above, in a regression process, a measured diffractionsignal measured off the patterned structure is compared to simulateddiffraction signals, which are iteratively generated based on sets ofprofile parameters, to get a convergence value for the set of profileparameters that generates the closest match simulated diffraction signalcompared to the measured diffraction signal. For a more detaileddescription of a regression-based process, see U.S. Pat. No. 6,785,638,titled METHOD AND SYSTEM OF DYNAMIC LEARNING THROUGH A REGRESSION-BASEDLIBRARY GENERATION PROCESS, issued on Aug. 31, 2004, which isincorporated herein by reference in its entirety.

In a library-based process, an optical metrology data store is generatedusing the optimized metrology model. The optical metrology data storehaving pairs of simulated diffraction signals and corresponding set ofprofile parameters. A detailed description of generating opticalmetrology data such as a library of simulated diffraction signals andcorresponding set of profile parameters is described in U.S. Pat. No.6,913,900, entitled GENERATION OF A LIBRARY OF PERIODIC GRATINGDIFFRACTION SIGNAL, by Niu, et al., issued on Sep. 13, 2005, and isincorporated in its entirety herein by reference.

In one embodiment, the profile of the patterned structure is determinedusing a measured diffraction signal and a subset of the metrology datastore that are within the fixed value in step 408. For example, if the aand b constant values of the N and K parameters were fixed in step 408,then the portion of the optical metrology data store used would be thesimulated diffraction signals and set of profile parameterscorresponding to fixed values of a and b.

In another embodiment, the profile of the patterned structure isdetermined using a measured diffraction signal and the entire opticalmetrology data store, i.e., searching the entire data space. Forexample, the profile of the patterned structure is determined using themeasured diffraction signal and the entire metrology data, i.e.,floating the a and b constants while searching for the best matchsimulated diffraction signal.

FIG. 5 is an exemplary architectural diagram of a real time profileestimator. A first fabrication cluster 916 is coupled to a metrologycluster 912. The first fabrication cluster 916 may include one or moreof a photolithography, etch, thermal processing system, metallization,implant, chemical vapor deposition, chemical mechanical polishing, orother fabrication unit. The first fabrication cluster 916 processes thewafer (not shown) through one or more process step. After each processstep, the wafer may be measured in the metrology cluster 912. Themetrology cluster 912 may be an inline or offline set of metrologydevices such as reflectometers, ellipsometers, hybridreflectometers/ellipsometers, scanning electron microscopes, sensors,and the like.

After measuring the wafer structure, the metrology cluster 912 transmitsdiffraction signals 811 to the model optimizer 904. The metrology modeloptimizer 904 uses the fabrication recipe input information andoptimization parameters 803, previous empirical structure profile data809 from the metrology data store 914 and measured diffraction signals811 from the metrology cluster 912 to create and optimize an opticalmetrology model of the structure measured. Recipe data 803 includematerials in the layers of the patterned and unpatterned structures inthe stack. Optimization parameters 803 include profile parameters,material refraction parameters, and metrology device parameters that arefloated in the optical metrology model. The model optimizer 904optimizes the optical metrology model based on the measured diffractionsignals 811 off the patterned structure, the recipe data andoptimization parameters 803, empirical data 809 from the metrology datastore 914 and creates an optimized optical metrology model 815transmitted to the real time profile estimator 918.

Referring to FIG. 5, the real time profile estimator 918 uses theoptimized optical metrology model 815, measured diffraction signals 817,and empirical metrology data 805 to determine the patterned structureprofile, critical dimension, and underlying thicknesses 843. Theempirical metrology data 805 may include fixed profile parameters (suchas pitch), the N and K parameters (such as constants a and b), and/ormetrology device parameters (such as angle of incidence and/or azimuthangle). The output of the real time profile estimator 918 is furtherselectively transmitted as data 841 to the first fabrication cluster916, transmitted as data 827 to the metrology data store 914 forstorage, and transmitted as data 845 to the second fabrication cluster930.

Data 841 transmitted to the first fabrication cluster 916 may include anunderlying film thicknesses, CD, and/or values of one or more profileparameters of the patterned structure. The underlying film thicknesses,CD, and/or values of one or more profile parameters of the patternedstructure may be used by the first fabrication cluster to alter one ormore process parameter such as focus and dose for a photolithographyfabrication cluster or dopant concentration for an ion implantationfabrication cluster. The data 845 transmitted to the second fabricationcluster 930 may include the patterned structure CD that may be used toalter the etchant concentration in an etch fabrication cluster or thedeposition time in a deposition cluster. The data 827 transmitted to themetrology data store comprises underlying film thicknesses, CD, and/orvalues of the profile parameters of the patterned structure togetherwith identification information such as wafer identification (ID), lotID, recipe, and patterned structure ID to facilitate retrieval for otherapplications.

Referring to FIG. 5, as mentioned above, the metrology data store 914may utilize identification information such as wafer ID, lot ID, recipe,and patterned structure ID as a means for organizing and indexing themetrology data. Data 813 from the metrology cluster 912 includesmeasured diffraction signals associated with identification for thewafer, lot, recipe, site or wafer location, and patterned structure orunpatterned structure. Data 809 from the metrology model optimizer 904includes variables associated with patterned structure profile,metrology device type and associated variables, and ranges used for thevariables floated in the modeling and values of variables that werefixed in the modeling. As mentioned above, empirical metrology data 805may include fixed profile parameters (such as pitch), the N and Kparameters (such as constants a and b), and/or metrology deviceparameters (such as angle of incidence and/or azimuth angle).

FIG. 6 is an exemplary architectural diagram of an embodiment forcreating and using a profile server to determine the profilecorresponding to a measured diffraction signal. FIG. 6 is similar toFIG.5 with two exceptions. First, the model optimizer 904 in FIG. 6 maycreate one of two data sets or both data sets in addition to optimizingthe metrology model. The first data set is a library of pairs ofsimulated diffraction signals and the corresponding set of profileparameters. The second data set is a trained machine learning system(MLS) where the MLS may be trained with a subset of the library, firstdata set mentioned above. The first and/or the second data set 819 arestored in the metrology data store 914. Secondly, the real time profileestimator 918 in FIG. 5 is replaced by a profile server 920 in FIG. 6.The profile server 920 uses either the library data set or the trainedMLS data set that is made available from the metrology model optimizer904. Alternatively, the profile server 920 may access the stored datasets in the metrology data store 914. The profile server 920 uses themeasured diffraction signals 817 from the metrology cluster 912, thelibrary or the trained MLS from the metrology data store 914 todetermine the underlying film thicknesses, CD, and profile parameters ofthe patterned structure 843. In addition, the profile server 920 may useempirical metrology data 805 comprising fixed profile parameters (suchas pitch), the N and K parameters (such as constants a and b), and/ormetrology device parameters (such as angle of incidence and/or azimuthangle) to set boundaries of the library or trained MLS that is used tofind the best match to the measured diffraction signal 817.

FIG. 7 is an exemplary architectural diagram for linking two or morefabrication systems with a metrology processor and a metrology datastore to determine profile parameters of patterned structures. A firstfabrication system 940 includes a model optimizer 942, a real timeprofile estimator 944, profile server 946, a fabrication cluster 948,and a metrology cluster 950. The first fabrication system 940 is coupledto a metrology processor 1010. The metrology processor 1010 is coupledto metrology data sources 1000, a metrology data store 1040, thefabrication host processors 1020, and to process simulator 1050.

Referring to FIG. 7, the components of the first fabrication system 940,i.e., the model optimizer 942, the real time profile estimator 944, theprofile server 946, the fabrication cluster 948, and the metrologycluster 950 are configured respectively to perform functions the same asthe corresponding devices described in FIG. 5 and FIG. 6. The metrologyprocessor 1010 receives metrology data 864 from the offline or remotemetrology data sources 1000. The offline metrology data sources 1000 maybe an offline cluster of metrology devices in the fabrication site suchas reflectometers, ellipsometers, SEM's and the like. The remotemetrology data sources 1000 may include a remote data server or remoteprocessor or website that provides metrology data for the application.Data 860 from the first fabrication system 940 to the metrologyprocessor 1010 may include the profile parameter ranges of the optimizedmetrology model and the generated data stores to determine the structureprofile parameters. The data stores 1040 may include a library of pairsof simulated diffraction signals and corresponding sets of profileparameters or a trained MLS system that can generate a set of profileparameters for an input measured diffraction signal. Data 870 from datastores 1040 to metrology processor 1010 includes a set of profileparameters and/or simulated diffraction signal. Data 860 from themetrology processor 1010 to the first metrology system 940 includesvalues of the profile parameters, material refraction parameters, andmetrology device parameters in order to specify the portion of the dataspace to be searched in the library or trained MLS store in themetrology data store 1040. Data 862 transmitted to and from the secondfabrication system 970 to the metrology processor 1010 are similar tothe data 860 transmitted to and from the first fabrication system 940.

Still referring to FIG. 7, data 866 transmitted to and from themetrology processor 1010 to the fabrication host processor 1020 mayinclude data related to the application recipe and process data measuredby the metrology clusters, 950 and 980, in the first and secondfabrication systems, 940 and 970. Data 868 such as profile parametervalues calculated using process simulators 1050 are transmitted to themetrology processor 1010 for use in setting selected variables of themetrology model to fixed values. Examples of process simulators areProlith™, Raphael™, Athena™, and the like. Alternatively, the profileparameter values may be used by the profile server 946 and 976 to definethe data space to search in the library or trained MLS store in themetrology data store 1040. The metrology data store 1040 in FIG. 7 isthe repository of metrology data and the metrology data is madeavailable to the first and/or the second fabrication system, 940 and970. As mentioned above, the first and/or second fabrication system, 940and 970, may include one or more of a photolithography, etch, thermalprocessing system, metallization, implant, chemical vapor deposition,chemical mechanical polishing, or other fabrication unit.

FIG. 8 is an exemplary flowchart for managing and utilizing metrologydata for patterned structure profile determination and automated processand equipment control. In step 1100, an optical metrology model iscreated and optimized using the method described in FIG. 3. In step1110, one or more data stores to determine the structure profileparameters are generated using the optimized optical metrology model.The data stores may include a library of pairs of simulated diffractionsignals and corresponding sets of profile parameters or a trained MLSsystem that can generate a set of profile parameters for an inputmeasured diffraction signal. In step 1120, data for profile parameters,material refraction parameters, and metrology device parameters areobtained. As mentioned above, selected profile parameters are those thatcan be made constant or fixed by using measured values or historicaldata for a similar wafer application. Values for material refractionparameters are the a and b constants for refractive index N andextinction coefficient K. Values for the metrology device parameters,such as angle of incidence, are obtained from the vendor specificationsof the metrology device. Values for azimuth angle are obtained from thesetup used in the diffraction measurement. In step 1130, the profileparameters, critical dimension (CD), and underlying thicknesses aredetermined using a measured diffraction signal.

Referring to FIG. 8, in step 1140, the profile parameters and materialdata of the structure is associated with identifying information.Identifying information includes site of the measured structure, wafer,wafer lot, run, application recipe, and other fabrication related data.In step 1150, the metrology data and associated identifying informationare stored in a metrology data store. The metrology data and/orassociated identifying information may be transmitted to a later or aprevious fabrication process step, in step 1160. In step 1170, thetransmitted metrology data and/or associated identifying information areused to modify at least one process variable of a later or a previousfabrication process step or an equipment control variable in theprevious, current or later fabrication process step. For example, avalue of the middle critical dimension (MCD) of a structure at an etchprocess step is transmitted to a previous lithography process step wherethe value of the MCD is used to modify a dose and/or focus of thestepper in a photolithography process step. Alternatively, a bottomcritical dimension (BCD) of a structure may be transmitted to an etchprocess step and the value of the BCD is used to modify the length ofetching or the concentration of the etchant. In another embodiment, theMCD may be sent to a current process, such as a post exposure bake (PEB)process step where the value of the MCD is used to modify thetemperature of the PEB process. The MCD may also be used to modify aprocess variable in the current process, such as the pressure in areaction chamber in an etch process.

In particular, it is contemplated that functional implementation of thepresent invention described herein may be implemented equivalently inhardware, software, firmware, and/or other available functionalcomponents or building blocks. For example, the metrology data store maybe in computer memory or in an actual computer storage device or medium.Other variations and embodiments are possible in light of aboveteachings, and it is thus intended that the scope of invention not belimited by this Detailed Description, but rather by Claims following.

1. A method of examining a patterned structure formed on a semiconductorwafer using an optical metrology model, the method comprising: a)creating an optical metrology model for the patterned structure, theoptical metrology model having profile parameters, material refractionparameters, and metrology device parameters; b) defining ranges ofvalues for the profile parameters, material refraction parameters, andmetrology device parameters; c) obtaining one or more measureddiffraction signals of the patterned structure; d) optimizing theoptical metrology model to obtain an optimized optical metrology modelusing the ranges of values defined in b) and the one or more measureddiffraction signals of the patterned structure obtained in c); e) for atleast one parameter from amongst the material refraction parameters andthe metrology device parameters, setting the at least one parameter to afixed value within the range of values for the at least one parameter;and f) determining at least one profile parameter of the patternedstructure using the optimized optical metrology model and the fixedvalue for the at least one parameter.
 2. The method of claim 1 whereinthe material refraction parameters include refractive indices (N)parameter and extinction coefficients (K) parameter.
 3. The method ofclaim 2 wherein the N parameter is represented by a vector a in theexpression N(λ, a) and the K parameter is represented by b in theexpression K(λ, b ), wherein λ is the wavelength.
 4. The method of claim2 wherein e) comprises: measuring N and K values of layers of thepatterned structure using inline optical metrology or offline opticalmetrology devices; and setting the N parameter and the K parameter tothe measured N and K values.
 5. The method of claim 4 wherein measuringthe N and K values of layers of the patterned structure includes:measuring an adjacent unpatterned area on the same wafer or anunpatterned area of a test wafer.
 6. The method of claim 5 wherein twoor more unpatterned areas are measured and a statistical average iscalculated.
 7. The method of claim 2 wherein the N parameter and the Kparameter in e) are set to empirical or theoretical N and K values forthe same material.
 8. The method of claim 1 wherein the metrology deviceparameters include angle of incidence, azimuth angle, angle ofincidence, and azimuth angle.
 9. The method of claim 2 wherein e)includes: setting the N and K parameters of layers of the patternedstructure to fixed values; and setting an angle of incidence parameterto a fixed value.
 10. The method of claim 2 wherein e) includes: settingthe N and K parameters of layers of the patterned structure to fixedvalues; and setting an azimuth angle parameter to a fixed value.
 11. Amethod of examining a patterned structure formed on a semiconductorwafer using an optical metrology model, the method comprising: a)creating an optical metrology model for the patterned structure, theoptical metrology model having profile parameters, material refractionparameters, and metrology device parameters; b) defining ranges ofvalues for the profile parameters, material refraction parameters, andmetrology device parameters; c) obtaining one or more measureddiffraction signals of the patterned structure; d) optimizing theoptical metrology model to obtain an optimized optical metrology modelusing the ranges of values and the one or more measured diffractionsignals of the patterned structure; e) creating an optical metrologydata store using the optimized optical metrology model, the opticalmetrology data store having pairs of simulated diffraction signals andcorresponding profile parameters, material refraction parameters, andmetrology device parameters; f) for at least one parameter from amongstthe material refraction parameters and the metrology device parameters,setting the at one parameter to a fixed value within the range of valuesfor the at least one parameter; g) obtaining another measureddiffraction signal different than the one or more measured diffractionsobtained in c); and h) for the measured diffraction signal obtained ing), determining a best-match simulated diffraction signal in the opticalmetrology data store using the fixed value for the at least oneparameter set in f).
 12. The method of claim 11 wherein the materialrefraction parameters include refractive indices (N) parameter andextinction coefficients (K) parameter.
 13. The method of claim 12wherein the N parameter is represented by a vector a in the expressionN(λ, a) and the K parameter is represented by b in the expression K(λ,b), wherein λ is the wavelength.
 14. The method of claim 12 wherein f)comprises: measuring N and K values of layers of the patterned structureusing inline optical metrology or offline optical metrology devices; andsetting the N parameter and the K parameter to the measured N and Kvalues.
 15. The method of claim 14 wherein measuring the N and K valuesof layers of the patterned structure includes: measuring an adjacentunpatterned area on the same wafer or an unpatterned area of a testwafer.
 16. The method of claim 15 wherein two or more unpatterned areasare measured and a statistical average is calculated.
 17. The method ofclaim 12 wherein the N parameter and the K parameter in e) are set toempirical or theoretical N and K values for the same material.
 18. Themethod of claim 11 wherein the metrology device parameters include angleof incidence, azimuth angle, angle of incidence, and azimuth angle. 19.The method of claim 12 wherein f) includes: setting the N and Kparameters of layers of the patterned structure to fixed values; andsetting an angle of incidence parameter to a fixed value.
 20. The methodof claim 12 wherein f) includes: setting the N and K parameters oflayers of the patterned structure to fixed values; and setting anazimuth angle parameter to a fixed value.
 21. The method of claim 11further comprising: extracting the profile parameters from the opticalmetrology data store corresponding to the best match simulateddiffraction signal.
 22. The method of claim 11 wherein determining thebest match of the measured diffraction signal comprises: using obtainedvalues of the material refraction indices to limit the range of thesearch in the optical metrology data store.
 23. The method of claim 11wherein determining the best match of the measured diffraction signalcomprises: using obtained values of the profile parameters to limit therange of the search in the optical metrology data store.
 24. The methodof claim 11 wherein determining the best match of the measureddiffraction signal comprises: using obtained values of the metrologydevice to limit the range of the search in the optical metrology datastore.
 25. The method of claim 11 wherein h) comprises: using the fixedvalue of the at least one parameter to limit the range of the search inthe optical metrology data store.
 26. The method of claim 11 wherein thepatterned structure is a repeating patterned structure defined by a unitcell.
 27. The method of claim 26 wherein the unit cell comprises one ormore of substructures that include island, posts, contact holes, orvias.
 28. The method of claim 11 wherein the optical metrology datastore includes a trained machine language learning system.
 29. Acomputer-readable storage medium containing computer executable code toexamine a patterned structure formed on a semiconductor wafer byinstructing the computer to operate as follows: a) creating an opticalmetrology model for the patterned structure, the optical metrology modelhaving profile parameters, material refraction parameters, and metrologydevice parameters; b) defining ranges of values for the profileparameters, material refraction parameters, and metrology deviceparameters; c) obtaining one or more measured diffraction signals of thepatterned structure; d) optimizing the optical metrology model to obtainan optimized optical metrology model using the ranges of values definedin b) and the one or more measured diffraction signals of the patternedstructure obtained in c); e) for at least one parameter from amongst thematerial refraction parameters and the metrology device parameters,setting the at least one parameter to a fixed value within the range ofvalues for the at least one parameter; and f) determining at least oneprofile parameter of the patterned structure using the optimized opticalmetrology model and the fixed value for the at least one parameter. 30.A computer-readable storage medium containing computer executable codeto examine a patterned structure formed on a semiconductor wafer byinstructing the computer to operate as follows: a) creating an opticalmetrology model for the patterned structure, the optical metrology modelhaving profile parameters, material refraction parameters, and metrologydevice parameters; b) defining ranges of values for the profileparameters, material refraction parameters, and metrology deviceparameters; c) obtaining one or more measured diffraction signals of thepatterned structure; d) optimizing the optical metrology model to obtainan optimized optical metrology model using the ranges of values and theone or more measured diffraction signals of the patterned structure; e)creating an optical metrology data store using the optimized opticalmetrology model, the optical metrology data store having pairs ofsimulated diffraction signals and corresponding profile parameters,material refraction parameters, and metrology device parameters; f) forat least one parameter from amongst the material refraction parametersand the metrology device parameters, setting the at one parameter to afixed value within the range of values for the at least one parameter;g) obtaining another measured diffraction signal different than the oneor more measured diffractions obtained in c); and h) for the measureddiffraction signal obtained in g), determining a best-match simulateddiffraction signal in the optical metrology data store using the fixedvalue for the at least one parameter set in f).